Few-Shot Learning (FSL) is to recognize novel visual concepts with several examples based on the acquired knowledge [1]}, [2]}, [3]}. It is also taken as a practice paradigm for meta-learning [4]}, [5]}, which learns the method to transfer old knowledge on novel tasks. However, vanilla FSL methods are limited by that old and novel tasks are from the same domain. Obviously, this setting is far from the demands of real-world applications. Thus, Cross-Domain Few-Shot Learning (CD-FSL) is proposed, in which acquired knowledge and novel tasks come from the source domain and the target domain respectively. Accordingly, the domain shift from the source domain to the target one is the main challenge in CD-FSL.
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